![]() The El Niño Southern Oscillation (ENSO) is known to be the primary driver of seasonal forecast skill across North America 7, 8, 9, 10, yet its signal-to-noise ratio is such that unexpected outcomes will occasionally occur by chance 4, 11, 12. In a seasonal forecasting context, teleconnections are best viewed as probabilistically loading the dice in favor of a certain outcome (i.e., dry versus wet conditions). Given that the economic costs of severe drought can frequently exceed $1B annually across California 5, 6, improving the skill of seasonal precipitation forecasts remains a top priority for water resource managers. As widely documented, the expected positive anomaly of precipitation across California and the Southwest under the major El Niño event of 2015/2016 did not eventuate as anticipated, and instead the devastating drought continued 4. During the recent severe California drought (years 2012–2016), the challenges for decision-makers under forecast uncertainty were highlighted. Individually, these storms have proven challenging to forecast at lead times beyond the weather time horizon 2, 3. Relatively low precipitation totals combined with high year-to-year variability are often received in the form of a relatively small number of atmospheric rivers across winter months 1. The climatology and variability of precipitation across the western United States present a unique seasonal forecasting challenge. We further show that this approach need not be considered a ‘black box’ by utilizing machine learning interpretability methods to identify the relevant physical processes that lead to prediction skill. For forecasting large-scale spatial patterns of precipitation across the western United States, here we show that these machine learning-based models are capable of competing with or outperforming existing dynamical models from the North American Multi Model Ensemble. After training on thousands of seasons of climate model simulations, the machine learning models are tested for producing seasonal forecasts across the historical observational period (1980-2020). To circumvent this issue, here we explore the feasibility of training various machine learning approaches on a large climate model ensemble, providing a long training set with physically consistent model realizations. ‡ The 1939-1940 season was missing the first five months of data (July through November).A barrier to utilizing machine learning in seasonal forecasting applications is the limited sample size of observational data for model training. † One or more months during the season was missing data for five or more days. The "Normal" or average seasonal precipitation for this station, provided by the National Centers for Environmental Information, is 13.91 inches, based upon the 30-year average for seasons from 1991 through 2020. * Average as calculated over all years of available rainfall data. See month-by-month numbers as season unfolds. NOTE: When most weather reporting sources cite weather in Los Angeles (without specifying where, exactly, in Los Angeles), these numbers are typically for the Downtown Los Angeles weather station (actually located on the USC campus).Īverage for seasons 1939 through 2021: 14.93 inches* Month-by-month numbers for latest season (2017-2018) Average - Santa Fe Dam (San Gabriel Valley) Average - Los Angeles International Airport ![]() Monthly Rainfall by Season - Hollywood Burbank Airport Overall Seasonal Average Burbank, California
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